Extending the actor-partner interdependence model to accommodate multivariate dyadic data using latent variables

This study extends the traditional Actor-Partner Interdependence model (APIM; Kenny, 1996) to incorporate dyadic data with multiple indicators reflecting latent constructs. Although the APIM has been widely used to model interdependence in dyads, the method and its applications have largely been limited to single sets of manifest variables. This article presents three extensions of the APIM that can be applied to multivariate dyadic data; a manifest APIM linking multiple indicators as manifest variables, a composite-score APIM relating univariate sums of multiple variables, and a latent APIM connecting underlying constructs of multiple indicators. The properties of the three methods in analyzing data with various dyadic patterns are investigated through a simulation study. It is found that the latent APIM adequately estimates dyadic relationships and holds reasonable power when measurement reliability is not too low, whereas the manifest APIM yields poor power and high type I error rates in general. The composite-score APIM, even though it is found to be a better alternative to the manifest APIM, fails to correctly reflect latent dyadic interdependence, raising inferential concerns. We illustrate the APIM extensions for multivariate dyadic data analysis by an example study on relationship commitment and happiness among married couples in Wisconsin. In cases where the measures are reliable reflections of psychological constructs, we suggest using the latent APIM for examining research hypotheses that discuss implications beyond observed variables. We conclude with stressing the importance of carefully examining measurement models when designing and conducting dyadic data analyses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).